CN114172184A - Wind power plant energy storage configuration method considering energy storage service life - Google Patents

Wind power plant energy storage configuration method considering energy storage service life Download PDF

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CN114172184A
CN114172184A CN202111516232.9A CN202111516232A CN114172184A CN 114172184 A CN114172184 A CN 114172184A CN 202111516232 A CN202111516232 A CN 202111516232A CN 114172184 A CN114172184 A CN 114172184A
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energy storage
wind power
service life
day
power plant
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CN114172184B (en
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袁亮
刘绪斌
柳张杰
韩华
孙尧
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Central South University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J15/00—Systems for storing electric energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
    • H02J7/345—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering using capacitors as storage or buffering devices
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2207/00—Indexing scheme relating to details of circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J2207/50—Charging of capacitors, supercapacitors, ultra-capacitors or double layer capacitors
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

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  • Power Engineering (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
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Abstract

The invention provides a wind power plant energy storage configuration method considering energy storage service life, which comprises the following steps: s1, analyzing the service life loss characteristics of two types of energy storage of a storage battery and a super capacitor, and establishing an actual service life annual value mathematical model; s2, analyzing and integrally considering the scheduling requirement characteristics of the wind power in the grid connection at the day-ahead and day-in stages, performing energy storage configuration by taking the annual operation income of the wind power plant as a target, and constructing an optimal configuration model of the integral energy storage of the wind power plant, wherein the energy storage service life is taken into consideration; and S3, solving by taking a certain actual wind power plant as example data to obtain a configuration result of the energy storage optimization configuration model. According to the invention, the energy storage service life loss and the wind power dispatching requirements at the day-ahead and day-in stages are taken into consideration, the characteristics and values of different types of energy storage are utilized, the economical efficiency of the wind power plant configuration energy storage is improved, and the development of new energy is effectively assisted.

Description

Wind power plant energy storage configuration method considering energy storage service life
Technical Field
The invention relates to the field of new energy power system technology and energy storage application, in particular to a wind power plant energy storage configuration method considering energy storage service life.
Background
The wind power in China is rapidly and continuously increased, the wind power gradually enters a large-scale wind power development stage, and the wind power accumulation installation in China reaches 2.1 hundred million kilowatts by 2019 years according to data sent by the national energy agency. However, due to characteristics of inverse peak shaving, randomness, volatility and the like of wind power, large-scale wind power development brings problems of a large amount of abandoned wind, grid-connected power fluctuation and the like, so that negative effects are brought to reliable and safe operation of a power system. Therefore, a series of requirements on wind power integration are made by national and regional governments, for example, in the aspect of wind power consumption, the national development and improvement committee and the energy agency jointly issue a 'clean energy consumption action plan (2018-2020)' and the wind power abandon rate of China is required to be controlled to be about 5% in 2020.
In order to solve the negative influence caused by the rapid development of wind power and meet the requirements of national and regional governments on wind power grid connection, the electric energy storage (short for energy storage) technology gradually becomes the current mainstream scheme due to the capability of bidirectional rapid electric power interaction and electric energy space-time migration. However, the current energy storage configuration has overlarge one-time investment, and the economical efficiency of the scheme of matching energy storage with wind power grid connection is poor due to the short service life of energy storage cycle.
The energy storage configuration of wind power takes a lot of achievements, however, most studies at the present stage configure energy storage aiming at a certain problem of wind abandonment or wind power fluctuation, and the connection between the wind abandonment and the wind power fluctuation and the corresponding scheduling stage is less discussed. In addition, the influence of the actual use state of the electric energy storage on the service life of the energy storage is less considered, namely the influence of the actual use state of the electric energy storage on the energy storage cost is neglected. Therefore, according to the requirement of wind power grid connection and by combining the technical characteristics of different types of electric energy storage, it is very important to perform energy storage optimization configuration to improve the economy of the wind power grid connection energy storage configuration.
Disclosure of Invention
In order to solve the technical problems, the method is a strong-use and good-economical wind power plant energy storage configuration method, and different types of energy storage capacities can be reasonably configured on the premise of ensuring the same wind abandon rate, so that the economical efficiency of wind power plant configuration energy storage is improved.
The invention relates to a wind power plant energy storage configuration method considering energy storage service life, which comprises the following steps:
s1, analyzing the service life loss characteristics of two types of energy storage of a storage battery and a super capacitor, and establishing an actual service life annual value mathematical model;
in the step S1, the service life loss of different types of energy storage is considered, the storage battery is influenced by the depth of discharge, and the cycle number B is countedDodAnd depth of discharge DodData and fitting to obtain BDod＝f(Dod) A mathematical relationship, recording the life loss corresponding to each cycle as
Figure BDA0003398252820000011
The life loss k of the day j can be obtained1,jThereby obtaining a storage battery equivalent annual service life mathematical model L1；
Figure BDA0003398252820000021
Figure BDA0003398252820000022
Wherein, BDod,j,sThe depth of charge and discharge corresponding to the cycle of the s time on the jth day, m represents the number of charge and discharge cycles of the electricity energy storage on the jth day, D is the number of operation days in one year, and the value is 365 without considering the influence of maintenance and outage;
the total number M of cycles of the super capacitor in the step S12The number of times of using and switching of the super capacitor on day j is k2，jEquivalent year life mathematical model L of super capacitor2；
Figure BDA0003398252820000023
S2, analyzing and integrally considering the scheduling requirement characteristics of the wind power in the grid connection at the day-ahead and day-in stages, performing energy storage configuration by taking the annual operation income of the wind power plant as a target, and constructing an optimal configuration model of the integral energy storage of the wind power plant, wherein the energy storage service life is taken into consideration;
in the step S2, an energy storage equivalent annual service life mathematical model is applied, and the integrity of wind power dispatching requirements is considered to obtain annual operation income
Figure BDA0003398252820000024
Aiming at the goal, a wind power plant integral energy storage optimization configuration model considering the energy storage service life is constructed, and the model comprises the annual wind curtailment penalty cost
Figure BDA0003398252820000025
Annual cost value and annual electricity selling income of energy storage configured in life cycle
Figure BDA0003398252820000026
Annual grid connection deviation assessment cost
Figure BDA0003398252820000027
Figure BDA0003398252820000028
Wherein,
Figure BDA0003398252820000029
the value of the ith type stored energy in the life cycle is shown, i takes 1 to represent a storage battery, i takes 2 to represent a super capacitor,
Figure BDA00033982528200000210
and
Figure BDA00033982528200000211
the power amounts respectively allocated to the storage battery and the super capacitor,
Figure BDA00033982528200000212
the storage battery and the super capacitor are respectively configured;
and S3, solving by taking a certain actual wind power plant as example data to obtain a configuration result of the energy storage optimization configuration model.
As a further improvement of the invention, the wind power field in the step S2 solves the inverse peak shaving performance of the wind power output at the day-ahead stage, and the storage battery has the advantage that the unit cost is more suitable than the power type ratio; and the wind power fluctuation is solved in the in-day stage, and the super capacitor is relatively suitable due to the rapid charge-discharge conversion capability.
As a further improvement of the present invention, in step S3, an actual wind farm is used as an application object of the present invention, and the calculation data is substituted and the fminunc function and the programming solution are called.
Compared with the prior art, the invention has the advantages that:
1) the invention fully considers the influence of the actual use process and state of the electric energy storage on the energy storage life and considers the characteristics of different types of energy storage, wherein the energy type energy storage is taken as an example of a storage battery, the power type energy storage is taken as an example of a super capacitor, the influence of the electric energy storage life is converted into the electric energy storage configuration cost, and an annual value mathematical model of the actual use life of the storage battery and the super capacitor is constructed. The established energy storage actual service life annual value mathematical model can provide more accurate basis for an energy storage configuration method.
2) According to the method, the anti-peaking property and the volatility of the wind power output of the wind power plant are fully analyzed, and the characteristics of the scheduling requirements of the wind power at the two stages of the day-ahead stage and the day-in stage are independently considered, so that the anti-peaking property of the wind power output of the wind power plant is mainly solved at the day-ahead stage of the wind power plant, and the wind power deviation problem caused by the volatility of the wind power at the end of the day-in stage is mainly solved at the day-in stage. Based on two-stage demand analysis in the day-ahead and day-in period, the regulation of the energy-demand type energy storage battery in the day-ahead stage and the regulation of the power-demand type energy storage super-capacitor in the day-in period are determined, and the integrality of the day-ahead and day-in demands is further considered, so that the storage battery and the super-capacitor can be more reasonably configured.
3) Based on the characteristics, the established energy storage actual service life annual-value mathematical model is applied, the integrity of wind power dispatching requirements at the day-ahead and day-in stages is considered, energy storage configuration is carried out by taking annual operation income of the wind power plant as a target, and the wind power plant integrity energy storage optimal configuration model considering the energy storage service life is established. On the basis, a certain actual wind power plant is taken as an application object of the method, and the practicability and effectiveness of the method are verified to a certain extent.
Drawings
FIG. 1 is a schematic flow chart of an implementation of a wind farm energy storage configuration method for accounting for energy storage service life in the embodiment;
FIG. 2 is a schematic structural diagram of a wind-storage combined power generation system in the embodiment;
FIG. 3 is a schematic diagram of wind power on a typical day of a month in the present embodiment;
FIG. 4 is a schematic diagram illustrating the effect of the battery configuration in the present embodiment;
FIG. 5 is a schematic diagram of a time zone for priority adjustment of a storage battery in the present embodiment;
FIG. 6 is a schematic diagram of the wind power consumption and predicted output on different typical days in the present embodiment;
fig. 7 is a schematic diagram of the predicted deviation output of wind power on different typical days in the embodiment.
Detailed Description
The invention is described in further detail below with reference to the following detailed description and accompanying drawings:
the method is a practical and good-economical wind power plant energy storage configuration method, and can reasonably configure different types of energy storage capacity on the premise of ensuring the same wind abandon rate, so that the economical efficiency of the wind power plant configuration energy storage is improved.
As shown in fig. 1, the method for reducing the cost of the wind power provider in the electricity balance market according to the embodiment includes the following steps:
s1, analyzing the service life loss characteristics of two types of energy storage of a storage battery and a super capacitor, and establishing an actual service life annual value mathematical model;
s2, analyzing and integrally considering the scheduling requirement characteristics of the wind power in the grid connection at the day-ahead and day-in stages, performing energy storage configuration by taking the annual operation income of the wind power plant as a target, and constructing an optimal configuration model of the integral energy storage of the wind power plant, wherein the energy storage service life is taken into consideration;
and S3, solving by taking a certain actual wind power plant as example data to obtain a configuration result of the energy storage optimization configuration model.
In this embodiment, the physical characteristics of the electrical energy storage can be divided into two types, namely energy type and power type, and the different types of electrical energy storage have respective suitable application characteristics. Energy type energy storage, such as a storage battery, has the advantage of lower unit cost than a power type energy storage, but frequent charging conversion of the energy type energy storage is harmful to the service life of the battery and is not favorable for the economy of the battery in the whole life cycle; the power type energy storage, such as a super capacitor, is suitable for suppressing frequent fluctuation of power due to its fast charge-discharge conversion capability, but is not suitable for large capacity configuration due to high configuration cost. Therefore, an actual service life annual value mathematical model needs to be established for different types of energy storage.
Under the conventional application environment, the service life of the storage battery is influenced by the discharge depth and the cycle life, and the actual service life is generally estimated through a relation between the cycle number and the discharge depth test, wherein the discharge depth is the ratio of the maximum electric storage capacity to the battery configuration capacity in a single charge-discharge process. The specific relationship is as follows:
BDod＝f(Dod) (1)
wherein, BDodTo charge and discharge with a depth DodThe corresponding cycle number, the life loss corresponding to each cycle is
Figure BDA0003398252820000041
Specific expressions are referred to in the literature.
The discharge depth at day j is related to the number of cycles and life loss in a single cycle k1,jThe following were used:
Figure BDA0003398252820000042
wherein, BDod,j,sAnd m represents the number of times of charging and discharging of the electrical energy storage in the j day.
The equivalent annual service life L of the storage battery can be deduced1Comprises the following steps:
Figure BDA0003398252820000043
wherein D is the number of days of operation in one year, and the value is 365 without considering the influence of maintenance and outage.
Under the conventional application environment, the super capacitor has the advantages of multiple cycle times and high power density, the service life of the super capacitor is mainly influenced by the cycle times, and the factory cycle times of the super capacitor are M2The value is 20 ten thousand times, and the use conversion times of the super capacitor on the jth day in one year is k2，jThen the equivalent annual service life L of the super capacitor can be deduced2Comprises the following steps:
Figure BDA0003398252820000044
for safe operation of electrical energy storage, certain charge and discharge power constraints need to be met:
Figure BDA0003398252820000045
wherein,
Figure BDA0003398252820000046
and
Figure BDA0003398252820000047
respectively charge and discharge power of the storage battery at the moment t,
Figure BDA0003398252820000048
and
Figure BDA0003398252820000049
respectively charge and discharge power P of the super capacitor at time t1 maxAnd
Figure BDA00033982528200000410
and respectively configuring power quantities for the storage battery and the super capacitor.
The energy stored by the electrical energy storage is the sum of the energy released/absorbed over a certain time, and its constraints are:
Figure BDA00033982528200000411
wherein E is1,tAnd E2,tThe storage capacities of the storage battery and the super capacitor at the time t are respectively,
Figure BDA00033982528200000412
and
Figure BDA00033982528200000413
respectively the charging efficiency and the discharging efficiency of the super capacitor,
Figure BDA0003398252820000051
and
Figure BDA0003398252820000052
and respectively charging and discharging efficiencies of the storage battery.
The general stored energy also needs to satisfy:
Figure BDA0003398252820000053
wherein,
Figure BDA0003398252820000054
and the storage battery and the super capacitor are respectively configured.
In the embodiment, a wind storage cogeneration system is constructed, the integrity of scheduling requirements of a wind power plant in the day ahead and in the day is considered, and the scheduling requirements are matched with different types of energy storage performance.
The wind power plant is configured with stored energy and then is constructed into the wind storage cogeneration system, compared with the wind power plant, the wind storage cogeneration system has higher flexibility, the output of the wind power plant can be adjusted, and the typical structure of the wind storage cogeneration system is shown in fig. 2.
In FIG. 2, PgridFor combined wind and stored energy, PwAnd outputting power for the wind power plant.
1) Day-ahead stage wind power plant energy storage configuration
The 'inverse peakedness' of the wind power plant output in the ante-day stage is mainly solved as shown in fig. 3, the ante-day output of the wind power plant at a certain day is used for diminishing, and the whole process is divided into a pre-declaration stage and a re-declaration stage.
The forecasting stage is that the wind power plant reports the predicted wind power output as the forecasting output to the upper power grid in a dispatching way, and the upper dispatching department issues the digestible wind power to the wind power plant after checking indexes of safety, reliability, economy and the like of the power grid. As shown in fig. 3, the wind output "back-peaking" can result in significant wind curtailment if not otherwise conditioned.
The reportation stage is that the wind power plant checks the wind power consumption rate, and if the wind power consumption rate meets the requirement, the pre-declared output is used as the declared output to be reported, scheduled and determined; if the wind power generation system does not meet the requirements, the wind power plant configures a storage battery according to the relation between the consumable wind power and the pre-declared output on the basis of considering the requirements of economy and wind power consumption rate, so that the wind power consumption meets the requirements, as shown in fig. 4.
2) Day-ahead stage wind power plant energy storage configuration
The in-day stage mainly solves the problem of wind power deviation caused by wind power volatility after the end of the day-ahead stage. Namely, the actual wind power output is as follows:
Figure BDA0003398252820000055
wherein,
Figure BDA0003398252820000056
and Δ Pw,tThe actual output, the predicted output and the predicted deviation of the wind power at the time t are respectively.
The output and the reported output of the actual wind storage system have deviation, and the deviation can be stabilized by adopting a super capacitor generally, namely:
Figure BDA0003398252820000057
wherein, Psb,tReporting output after being adjusted by the storage battery at the time t;
Figure BDA0003398252820000058
respectively the charging and discharging power of the storage battery at the moment t in the day ahead;
Figure BDA0003398252820000059
respectively is the charge and discharge power of the super capacitor at the time t; pqw,tAbandoning wind power for t moment; delta Ps,tAnd (5) carrying out real-time later t-moment grid connection deviation for adjustment measures.
But considering only the suppression deviation of the super capacitor alone makes the configured capacity larger and the economical efficiency is not considerable. Therefore, the implementation case fully considers the configuration of the storage battery and the super capacitor, namely the operation section of the storage battery, and can preferentially modify the operation state of the storage battery and eliminate the deviation amount; the non-action time of the storage battery is eliminated by the configured super capacitor, and the schematic diagram is shown in fig. 5.
Of course, when the battery participates in the deviation elimination, the following relationship is required to be satisfied:
Figure BDA0003398252820000061
Figure BDA0003398252820000062
wherein,
Figure BDA0003398252820000063
and respectively adjusting the charging and discharging power of the storage battery at the moment t after the output is adjusted in the day.
In addition, in order to keep the service life of the storage battery, the charging and discharging state of the storage battery is not changed by daily adjustment, namely, the following conditions are met:
Figure BDA0003398252820000064
according to the method, the dispatching requirement characteristics of the wind power in the day-ahead and day-in stages of grid connection are analyzed and considered integrally, a wind power plant integral energy storage optimization configuration model considering the energy storage service life is established, energy storage configuration is carried out by taking the annual operation income of the wind power plant as a target, and the annual value, the annual wind abandoning penalty cost, the grid connection deviation check result and the annual electricity selling income under the actual service life of the configured energy storage are considered.
The cost annual value under the configured energy storage actual service life is mainly composed of the acquisition cost of the battery body, the cost of auxiliary equipment, the operation and maintenance cost and the recovery residual value.
1) The purchase cost of the body is as follows:
Figure BDA0003398252820000065
wherein λ isi,EAnd λi,PAnd respectively purchasing unit prices of unit capacity and power of the ith type of electric energy storage.
Figure BDA0003398252820000066
Pi maxCapacity and power configured for the i-th class of electrical energy storage, respectively.
Considering the influence of the currency time value, the annual value of the purchase cost of the body of the electric energy storage is as follows:
Figure BDA0003398252820000067
Figure BDA0003398252820000068
wherein r is the reduction rate and takes a value of 0.08; l isiThe actual service life of the i-th type of electric energy storage is prolonged.
2) Cost of auxiliary equipment:
auxiliary equipment cost C of class i electric energy storagei,supCan be described as:
Figure BDA0003398252820000069
wherein,
Figure BDA00033982528200000610
is the price per unit capacity of the i-th class battery auxiliary equipment.
Similarly, the annual value of the cost of the auxiliary equipment
Figure BDA0003398252820000071
Comprises the following steps:
Figure BDA0003398252820000072
3) cost of operation and maintenance
Annual operation and maintenance cost of electricity energy storage is fixed by annual operation and maintenance cost
Figure BDA0003398252820000073
Annual variable operating maintenance costs
Figure BDA0003398252820000074
The operation and maintenance cost of the i-th battery can be expressed as:
Figure BDA0003398252820000075
the annual fixed cost mainly reflects the input expenses of manpower and management, which are independent of the daily battery operating state, mainly depending on the type of energy storage and the configured power.
Figure BDA0003398252820000076
Wherein,
Figure BDA0003398252820000077
and fixing the annual operation and maintenance cost coefficient of the i-th battery.
The variable cost of year mainly reflects the expense that the battery needs in daily operation, is influenced by daily operating condition, mainly describes by the size of the energy of handling every day, considers the influence of time value factor, can be expressed as:
Figure BDA0003398252820000078
wherein,
Figure BDA0003398252820000079
the operation and maintenance cost is the unit charge and discharge capacity of the ith battery; y isi,jThe charge/discharge amount of the ith battery on the j day is shown.
4) And (3) recovering a residual value:
at the end of the theoretical life of the battery, the yield can be obtained through recycling, and the recycling year value can be expressed as:
Figure BDA00033982528200000710
wherein, κi,recAnd taking 0.15 as the recovery coefficient of the i-th stored energy.
In summary, the annual cost value of the configuration of the electrical energy storage in the life cycle can be expressed as:
Figure BDA00033982528200000711
the annual wind abandonment penalty cost is as follows:
Figure BDA00033982528200000712
wherein,
Figure BDA00033982528200000713
penalizing costs for annual wind abandonment; k is a radical ofqwTaking the value of the wind power penalty coefficient as 2; lambda [ alpha ]grid,tThe unit price of the wind power on-line at the time t; and delta t is the sampling time length and takes 0.15 h.
The evaluation cost of the grid-connected deviation is as follows:
Figure BDA00033982528200000714
wherein,
Figure BDA00033982528200000715
the annual grid connection deviation checking cost is saved; k is a radical ofpcThe deviation evaluation coefficient is taken as 3;
Figure BDA00033982528200000716
taking 2 percent of the predicted force as the allowable deviation amount at the time t on the j day
The annual electricity selling income is as follows:
Figure BDA0003398252820000081
wherein,
Figure BDA0003398252820000082
earning for annual electricity sales; lambda [ alpha ]grid,tThe unit price of the wind power grid at the time t.
The objective function is then:
Figure BDA0003398252820000083
in order to meet the requirement range of the air abandonment rate control, an annual air abandonment rate constraint is set:
Figure BDA0003398252820000084
wherein,
Figure BDA0003398252820000085
respectively predicting the output and the wind power which can be consumed at the jth moment of the jth day; gamma ray qfl5 percent of the air is taken for controlling the level of the abandoned air.
Further, the implementation case constraints also include formula (5) -formula (7) and formula (8) -formula (12).
In the embodiment, a certain wind power plant in the middle of China is selected to perform electricity energy storage configuration and correlation analysis, wind power predicted output, wind power capable of being consumed and predicted output deviation of the wind power plant in 2018 years are used as data samples, and for convenience in analysis and model solution, data are clustered by taking days as units to obtain 4 types of typical days, as shown in fig. 6-7; the storage battery is selected from a lithium iron storage battery and a super capacitor, and relevant parameters of the storage battery are shown in table 1; the unit price of the wind power grid is 0.6 yuan/kWh.
TABLE 1 cost parameters of electrical energy storage
Figure BDA0003398252820000086
In order to compare the effectiveness of the proposed electrical energy storage configuration scheme, a total of 3 schemes are designed.
Case 1: the embodiment provides an energy configuration scheme; case 2: scheduling demand integrity is not considered compared to Case 1; case 3: compared with Case1, the actual service life constraint is not considered, but the actual service life year is converted according to the actual service condition. Calling fminuc function and programming to solve each scheme, the results are shown in table 2:
table 2 different scheme energy storage configuration results
Figure BDA0003398252820000087
Figure BDA0003398252820000091
Analysis table 2 shows that scheme 1 can effectively prolong the service life of the super capacitor and reduce the configuration of the power and the capacity of the super capacitor compared with scheme 2, but because the storage battery in scheme 1 needs to take wind power consumption and deviation elimination into consideration in a certain period of time, the power and the capacity of the storage battery needing to be configured are improved. As can be seen by comparing scheme 1 with scheme 3, scheme 3 does not consider the service life constraint, and although the configuration can reduce the capacity and power of the storage battery, the storage battery operates in a deep charge-discharge state, so that the actual service life of the storage battery is reduced.
TABLE 3 comparison of different economics
Figure BDA0003398252820000092
By combining the tables 1 and 3, the scheme 1 and the scheme 2 are compared, so that the scheme considers that the scheme for integrally configuring the scheduling demand in the day-before-day reduces the energy storage configuration and annual operation cost under the condition that the wind power consumption and the deviation elimination are equivalent, and the validity of the scheme for configuring the electricity energy storage is verified; comparing schemes 1 and 3, it can be seen that the annual operation cost of the electric energy storage configuration constrained by the actual service life is considered to be lower, the annual operation income is improved, and the effectiveness of the invention in consideration of the actual service life is verified.
In the invention, the wind power plant energy storage configuration method considering the energy storage service life is adopted aiming at the wind power plant energy storage configuration which considers the influence of the actual service life of the energy storage on the energy storage cost less and considers the integrality of the scheduling requirements of the day-ahead and day-in phases of wind power integration in a missing manner. The invention can lead the storage battery and the super capacitor to be more reasonably configured, improves the benefit of a configured energy storage party to a certain extent, and can provide assistance for the development and application of new energy.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made according to the technical spirit of the present invention are within the scope of the present invention as claimed.

Claims (3)

1. A wind power plant energy storage configuration method considering energy storage service life is characterized by comprising the following steps:
s1, analyzing the service life loss characteristics of two types of energy storage of a storage battery and a super capacitor, and establishing an actual service life annual value mathematical model;
in the step S1, the service life loss of different types of energy storage is considered, the storage battery is influenced by the depth of discharge, and the cycle number B is countedDodAnd depth of discharge DodData and fitting to obtain BDod＝f(Dod) A mathematical relationship, recording the life loss corresponding to each cycle as
Figure FDA0003398252810000011
The life loss k of the day j can be obtained1,jThereby obtaining a storage battery equivalent annual service life mathematical model L1；
Figure FDA0003398252810000012
Figure FDA0003398252810000013
Wherein, BDod,j,sThe depth of charge and discharge corresponding to the cycle of the s time on the jth day, m represents the number of charge and discharge cycles of the electricity energy storage on the jth day, D is the number of operation days in one year, and the value is 365 without considering the influence of maintenance and outage;
the total number M of cycles of the super capacitor in the step S12The number of times of using and switching of the super capacitor on day j is k2，jEquivalent year life mathematical model L of super capacitor2；
Figure FDA0003398252810000014
S2, analyzing and integrally considering the scheduling requirement characteristics of the wind power in the grid connection at the day-ahead and day-in stages, performing energy storage configuration by taking the annual operation income of the wind power plant as a target, and constructing an optimal configuration model of the integral energy storage of the wind power plant, wherein the energy storage service life is taken into consideration;
in the step S2, an energy storage equivalent annual service life mathematical model is applied, and the integrity of wind power dispatching requirements is considered to obtain annual operation income
Figure FDA0003398252810000015
Aiming at the goal, a wind power plant integral energy storage optimization configuration model considering the energy storage service life is constructed, and the model comprises the annual wind curtailment penalty cost
Figure FDA0003398252810000016
Annual cost value and annual electricity selling income of energy storage configured in life cycle
Figure FDA0003398252810000017
Annual grid connection deviation assessment cost
Figure FDA0003398252810000018
Figure FDA0003398252810000019
Wherein,
Figure FDA00033982528100000110
the value of the ith type stored energy in the life cycle is shown, i takes 1 to represent a storage battery, i takes 2 to represent a super capacitor,
Figure FDA00033982528100000111
and
Figure FDA00033982528100000112
the power amounts respectively allocated to the storage battery and the super capacitor,
Figure FDA00033982528100000113
the storage battery and the super capacitor are respectively configured;
and S3, solving by taking a certain actual wind power plant as example data to obtain a configuration result of the energy storage optimization configuration model.
2. The wind farm energy storage configuration method taking energy storage service life into consideration of claim 1, characterized by comprising the following steps: in the step S2, the problem of the anti-peak shaving performance of the wind power output is solved in the wind power field day-ahead stage, and the storage battery has the advantages that the unit cost is lower than the power type ratio and is more appropriate; and the wind power fluctuation is solved in the in-day stage, and the super capacitor is relatively suitable due to the rapid charge-discharge conversion capability.
3. The wind farm energy storage configuration method taking energy storage service life into consideration of claim 1, characterized by comprising the following steps: in the step S3, an actual wind farm is used as an application object of the present invention, and the calculation data is substituted and fminunc function and programming solution are called.
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